similar to: calling functions

Hi, I have 3 lists, x, y, z and I'd like to perform a calculation over all the lists simultaneously. If it were a single list I could use lapply, but for more than one list I'm using a for loop. Is there an idiom that would let me use something like lapply, but the function specified to lappy would have access to an element from each list? (In Python, I would have used for a,b,c in

Dear All, I wrote a function for cluster analysis to compute cophenetic correlations between dissimilarity matrices (using the VEGAN library) and cluster analyses of every possible clustering algorithm (SEE ATTACHED) http://old.nabble.com/file/p26288610/cor.coef.R cor.coef.R . As it is now, it is extremely long, and for the future I was hoping to find a more efficient way of doing this sort of

useR's, This is a follow up question to one I previously asked. Consider the 3-element list below > res [[1]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] NA NA NA 1.25 0.25 0.75 NA NA NA NA NA NA 1.25 0.25 [2,] 1.25 0.25 0.75 NA NA NA NA NA NA 1.25 0.25 0.75 NA NA [3,] NA NA NA NA NA

Folks, I have three dataframes storing some information about two currency pairs, as follows: R> a EUR-USD NOK-SEK 1.23 1.33 1.22 1.43 1.26 1.42 1.24 1.50 1.21 1.36 1.26 1.60 1.29 1.44 1.25 1.36 1.27 1.39 1.23 1.48 1.22 1.26 1.24 1.29 1.27 1.57 1.21 1.55 1.23 1.35 1.25 1.41 1.25 1.30 1.23 1.11 1.28 1.37 1.27 1.23 R> b EUR-USD NOK-SEK 1.23 1.22 1.21 1.36 1.28 1.61 1.23 1.34 1.21 1.22

Vectorize is defined to return a function that acts as if 'mapply' was called. So we have: > mapply(dput,1:2) # mapply form 1L # calls dput on each element of 1:2 2L [1] 1 2 > Vectorize(dput)(1:2) # Vectorize form 1L # same behavior 2L [1] 1 2 Same thing with a named argument: >

Hi R'ers- I'll believe this can be done and that I'm just not clever enough. I'm trying to do this in 2D, but i think we have the same problem in 1D. #Assume you have some 1D time series d<-rnorm(100) #Say you want to get the average over intervals of different lengths #like from time 10 to time 24 and time 51 to time 86 starts <- c(10,51) ends <- c(24,86) results

Hi all, I've been struggling learning R and need to turn to the list again. I've got a dataset (comma-delimited file) with the following fields: recid, latitude, longitude, population, dwelling and age. For each observation, I'd like to calculate the total number of people and dwellings and average age within 2 k.m. Distance could be Euclidean, however, a proper distance

Hello all I would like to ask your help use mapply. I have a function called findCell that takes two arguments(x,sr) where x is a vector of size two (e.g x<-c(2,3) and sr is a matrix. I would like to call many times the findCell function (thus I need mapply) for different x inputs but always for the same sr. as x is a vector of size two (two cells) I want to pass inside inside the following

Dear R-users I am trying to "vectorize" a function so that it can handle two vectors of inputs. I want the function to use phi (a function), k[1] and t[1] for the first price, and so on for the second, third and fourth price. I tried to do the mapply, but I dont know how to specify to R what input I want to be vectors (k and t)(see in the bottom what I tried). I have read the help file,

Dear all, I have a large dataset which I hope to reduce in size, to make it more useable. I hope to do this by taking an average of each 60 x 60 blockof values and forming a new data frame out of the averaged values. How would I go about taking averages of 60 x 60 'blocks' in R, and cycling through the whole dataset, recording each calculated value in a new table/data frame? Many thanks

Now that netflix is in Australia, I wouldn't mind giving it a burl. It's working fine on my fedora-21 lappy with chrome-40 but not on our centos-6 mythtv setup even with chrome-41. I understand the difference might be the version of NSS - fedora-21 has 3.17 while centos is stuck at 3.16. Other than that, I'm flumoxed. Anyone got netflix running on centos-6? Cheers Bob

[Apologies to Gabor, who I sent a personal copy of the reply erroneously instead of posting to List directly] [...] > Perhaps what you really intend is to > take the average over those elements in each row of the first matrix which correspond to 1's in the second in the corresponding > row of the second. In that case its just: > > rowSums(newtest * goldstandard) /

Hi Everyone, I have a list of vectors like this (in this case it's 3 vectors but assume the vector count and the length of each vector is not known): [[1]] [1] 9 5 7 2 14 4 4 3 [[2]] [1] 3 6 25 2 14 3 3 4 [[3]] [1] 28 4 14 3 14 2 4 5 What I want to do is take the average vertically. Thus I want to do 9+3+28 /3, 5+6+4 /3, etc... and then have it return a vector. I'm

Dear group, Here is my data frame: > dput(u) structure(list(DESCRIPTION = structure(c(2L, 5L, 6L, 7L, 9L, 11L, 12L, 15L, 14L, 16L, 1L, 10L, 3L, 4L, 13L, 8L, 17L), .Label = c("COFFEE C Jul/10", "COPPER May/10", "CORN Jul/10", "CORN May/10", "COTTON NO.2 Jul/10", "CRUDE OIL miNY May/10", "GOLD Jun/10", "HENRY HUB

Dear falks, here I have written following function :   fn <- Vectorize(function(x = 1:3, y = 3:6) {         x <- matrix(x, nrow=1)         y <- matrix(y, ncol=1)         dat <- apply(x, 2, function(xx) {                               apply(y, 1, function(yy) {                                   return(xx + yy) } ) })         return(dat)}, SIMPLIFY = TRUE) If I run this function, I got

typically, the apply family wants you to use vectors to run functions on. However, I have a function, kruskal.test, that requires 2 arguments. kruskal.test(Herb.df$Score,Herb.df$Year) This easily computes the KW ANOVA statistic for any difference across years.... However, my data has multiple sites on which KW needs to be run... here's the data: Herb.df<-

I am looking for some help at removing low-frequency components from a signal, through Moving Average on a sliding window. I understand thiis is a smoothing procedure that I never done in my life before .. sigh. I searched R archives and found "rollmean", "MovingAverages {TTR}", "SymmetricMA". None of the above mantioned functions seems to accept the smoothing

I use Windows, R version 2.4.1. I have a dataset in which columns 1-3 are replicates, 4-6, are replicates, etc. I need to calculate an average for every set of replicates (columns 1-3, 4-6, 7-9, etc.) AND each set of replicates should be averaged every 14 rows (for more detail, to measure fruit color using a spectrometer, I recorded three readings per fruit -replicates- that I need to average to

Dear ExpRts, I would like to perform a function with two arguments over the rows of two matrices. There are a couple of *applys (including mApply in Hmisc) but I haven't found out how to do it straightforward. Applying to row indices works, but looks like a poor hack to me: sens <- function(test, gold) { if (any(gold==1)) { sum(test[which(gold==1)]/sum(which(gold==1))) } else NA }

Hi everyone, I just wiped XP off my old Dell D610 and loaded Ubuntu 10.10... it runs like a brand new machine! (If if weren't for compatibility issues I would do the same to my netbook right away!) Anyway to test out Wine I installed IL-2 1946, patched up to 4.10m. With a little tweaking it is running like a dream, save one minor issues. Occasionally the program hiccups with what looks